Advertisement

Updating for a new setting

  • E.W. Steyerberg
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Background

A prediction model ideally provides valid predictions of outcome for individual patients at another setting than where the model was developed, e.g. differing in time and place. The validity of predictions can be assessed by comparing observed outcomes and predictions when empirical data from this setting are available. Various patterns of invalidity may however be observed as we have seen in the previous chapter. Detection of calibration-in-the-large problems should have top priority since miscalibration can cause systematically wrong decision making with the model (negative net benefit). Obviously, we may subsequently aim to update the model to improve predictions for future patients from the new setting. We discuss several approaches for updating a previously developed model. The risk is that simply re-estimating all regression coefficients in a model might replace reliable but slightly biased estimates by unbiased but very unreliable ones, particularly if the validation data set is relatively small.

We start with considering updating methods that focus on re-calibration (re-estimation of the intercept and/or updating of the slope of the linear predictor). Next, we turn to more structural model revisions (re-estimation of some or all regression coefficients, model extension with more predictors). For illustration we consider case studies with updating of a previously developed logistic regression model, a regression tree, and a previously developed Cox regression model. We conclude that parsimonious updating methods may often be preferable to more extensive model revisions, which should only be attempted with relatively large validation samples, in combination with shrinkage of differences between the updated model and the previously developed model.

Keywords

Validation Sample International Prognostic Index Linear Predictor Weibull Model Shrinkage Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • E.W. Steyerberg
    • 1
  1. 1.Department of Public HealthErasmus MCRotterdamThe Netherlands

Personalised recommendations